






"""simple docstring"""


import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						lowerCAmelCase   :int						=			VideoMAEConfig()

						set_architecture_configs(a__     ,      a__ )

						if "finetuned" not in model_name:
												lowerCAmelCase   :Dict						=			False

						if "finetuned" in model_name:
												lowerCAmelCase   :int						=			'huggingface/label-files'
												if "kinetics" in model_name:
																		lowerCAmelCase   :Any						=			4_00
																		lowerCAmelCase   :Union[str, Any]						=			'kinetics400-id2label.json'
												elif "ssv2" in model_name:
																		lowerCAmelCase   :Union[str, Any]						=			1_74
																		lowerCAmelCase   :Optional[int]						=			'something-something-v2-id2label.json'
												else:
																		raise ValueError('Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned.' )
												lowerCAmelCase   :Tuple						=			json.load(open(hf_hub_download(a__     ,      a__     ,      repo_type='dataset' )     ,      'r' ) )
												lowerCAmelCase   :Tuple						=			{int(a__ ): v for k, v in idalabel.items()}
												lowerCAmelCase   :List[str]						=			idalabel
												lowerCAmelCase   :List[str]						=			{v: k for k, v in idalabel.items()}

						return config


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						if "small" in model_name:
												lowerCAmelCase   :Optional[int]						=			3_84
												lowerCAmelCase   :Optional[int]						=			15_36
												lowerCAmelCase   :Any						=			12
												lowerCAmelCase   :Any						=			16
												lowerCAmelCase   :Tuple						=			12
												lowerCAmelCase   :List[Any]						=			3
												lowerCAmelCase   :Optional[int]						=			1_92
												lowerCAmelCase   :List[str]						=			7_68
						elif "large" in model_name:
												lowerCAmelCase   :str						=			10_24
												lowerCAmelCase   :List[Any]						=			40_96
												lowerCAmelCase   :Tuple						=			24
												lowerCAmelCase   :Tuple						=			16
												lowerCAmelCase   :str						=			12
												lowerCAmelCase   :Optional[int]						=			8
												lowerCAmelCase   :List[Any]						=			5_12
												lowerCAmelCase   :Optional[int]						=			20_48
						elif "huge" in model_name:
												lowerCAmelCase   :Optional[Any]						=			12_80
												lowerCAmelCase   :Dict						=			51_20
												lowerCAmelCase   :Tuple						=			32
												lowerCAmelCase   :Optional[Any]						=			16
												lowerCAmelCase   :Union[str, Any]						=			12
												lowerCAmelCase   :int						=			8
												lowerCAmelCase   :str						=			6_40
												lowerCAmelCase   :str						=			25_60
						elif "base" not in model_name:
												raise ValueError('Model name should include either "small", "base", "large", or "huge"' )


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						if "encoder." in name:
												lowerCAmelCase   :Optional[Any]						=			name.replace('encoder.'     ,      '' )
						if "cls_token" in name:
												lowerCAmelCase   :Dict						=			name.replace('cls_token'     ,      'videomae.embeddings.cls_token' )
						if "decoder_pos_embed" in name:
												lowerCAmelCase   :Tuple						=			name.replace('decoder_pos_embed'     ,      'decoder.decoder_pos_embed' )
						if "pos_embed" in name and "decoder" not in name:
												lowerCAmelCase   :int						=			name.replace('pos_embed'     ,      'videomae.embeddings.position_embeddings' )
						if "patch_embed.proj" in name:
												lowerCAmelCase   :Tuple						=			name.replace('patch_embed.proj'     ,      'videomae.embeddings.patch_embeddings.projection' )
						if "patch_embed.norm" in name:
												lowerCAmelCase   :Optional[Any]						=			name.replace('patch_embed.norm'     ,      'videomae.embeddings.norm' )
						if "decoder.blocks" in name:
												lowerCAmelCase   :Dict						=			name.replace('decoder.blocks'     ,      'decoder.decoder_layers' )
						if "blocks" in name:
												lowerCAmelCase   :Optional[Any]						=			name.replace('blocks'     ,      'videomae.encoder.layer' )
						if "attn.proj" in name:
												lowerCAmelCase   :str						=			name.replace('attn.proj'     ,      'attention.output.dense' )
						if "attn" in name and "bias" not in name:
												lowerCAmelCase   :Optional[int]						=			name.replace('attn'     ,      'attention.self' )
						if "attn" in name:
												lowerCAmelCase   :List[Any]						=			name.replace('attn'     ,      'attention.attention' )
						if "norm1" in name:
												lowerCAmelCase   :Any						=			name.replace('norm1'     ,      'layernorm_before' )
						if "norm2" in name:
												lowerCAmelCase   :Tuple						=			name.replace('norm2'     ,      'layernorm_after' )
						if "mlp.fc1" in name:
												lowerCAmelCase   :Union[str, Any]						=			name.replace('mlp.fc1'     ,      'intermediate.dense' )
						if "mlp.fc2" in name:
												lowerCAmelCase   :Tuple						=			name.replace('mlp.fc2'     ,      'output.dense' )
						if "decoder_embed" in name:
												lowerCAmelCase   :Optional[int]						=			name.replace('decoder_embed'     ,      'decoder.decoder_embed' )
						if "decoder_norm" in name:
												lowerCAmelCase   :str						=			name.replace('decoder_norm'     ,      'decoder.decoder_norm' )
						if "decoder_pred" in name:
												lowerCAmelCase   :List[str]						=			name.replace('decoder_pred'     ,      'decoder.decoder_pred' )
						if "norm.weight" in name and "decoder" not in name and "fc" not in name:
												lowerCAmelCase   :Any						=			name.replace('norm.weight'     ,      'videomae.layernorm.weight' )
						if "norm.bias" in name and "decoder" not in name and "fc" not in name:
												lowerCAmelCase   :Dict						=			name.replace('norm.bias'     ,      'videomae.layernorm.bias' )
						if "head" in name and "decoder" not in name:
												lowerCAmelCase   :Dict						=			name.replace('head'     ,      'classifier' )

						return name


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						for key in orig_state_dict.copy().keys():
												lowerCAmelCase   :List[Any]						=			orig_state_dict.pop(a__ )

												if key.startswith('encoder.' ):
																		lowerCAmelCase   :str						=			key.replace('encoder.'     ,      '' )

												if "qkv" in key:
																		lowerCAmelCase   :Dict						=			key.split('.' )
																		if key.startswith('decoder.blocks' ):
																								lowerCAmelCase   :str						=			config.decoder_hidden_size
																								lowerCAmelCase   :int						=			int(key_split[2] )
																								lowerCAmelCase   :Any						=			'decoder.decoder_layers.'
																								if "weight" in key:
																														lowerCAmelCase   :List[str]						=			val[:dim, :]
																														lowerCAmelCase   :List[Any]						=			val[dim : dim * 2, :]
																														lowerCAmelCase   :Dict						=			val[-dim:, :]
																		else:
																								lowerCAmelCase   :int						=			config.hidden_size
																								lowerCAmelCase   :Optional[int]						=			int(key_split[1] )
																								lowerCAmelCase   :Union[str, Any]						=			'videomae.encoder.layer.'
																								if "weight" in key:
																														lowerCAmelCase   :List[Any]						=			val[:dim, :]
																														lowerCAmelCase   :Dict						=			val[dim : dim * 2, :]
																														lowerCAmelCase   :str						=			val[-dim:, :]
												else:
																		lowerCAmelCase   :List[str]						=			val

						return orig_state_dict


def        UpperCAmelCase      (					):





						'''simple docstring'''






						lowerCAmelCase   :int						=			hf_hub_download(
						    repo_id='hf-internal-testing/spaghetti-video'     ,      filename='eating_spaghetti.npy'     ,      repo_type='dataset' )
						lowerCAmelCase   :str						=			np.load(a__ )
						return list(a__ )


def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :List[Any]						=			get_videomae_config(a__ )

						if "finetuned" in model_name:
												lowerCAmelCase   :str						=			VideoMAEForVideoClassification(a__ )
						else:
												lowerCAmelCase   :Optional[Any]						=			VideoMAEForPreTraining(a__ )

						# download original checkpoint, hosted on Google Drive
						lowerCAmelCase   :List[Any]						=			'pytorch_model.bin'
						gdown.cached_download(a__     ,      a__     ,      quiet=a__ )
						lowerCAmelCase   :Tuple						=			torch.load(a__     ,      map_location='cpu' )
						if "model" in files:
												lowerCAmelCase   :List[Any]						=			files['model']
						else:
												lowerCAmelCase   :Union[str, Any]						=			files['module']
						lowerCAmelCase   :Union[str, Any]						=			convert_state_dict(a__     ,      a__ )

						model.load_state_dict(a__ )
						model.eval()

						# verify model on basic input
						lowerCAmelCase   :str						=			VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]     ,      image_std=[0.5, 0.5, 0.5] )
						lowerCAmelCase   :str						=			prepare_video()
						lowerCAmelCase   :Any						=			image_processor(a__     ,      return_tensors='pt' )

						if "finetuned" not in model_name:
												lowerCAmelCase   :List[str]						=			hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos'     ,      filename='bool_masked_pos.pt' )
												lowerCAmelCase   :Optional[int]						=			torch.load(a__ )

						lowerCAmelCase   :Optional[int]						=			model(**a__ )
						lowerCAmelCase   :List[Any]						=			outputs.logits

						lowerCAmelCase   :str						=			[
						    'videomae-small-finetuned-kinetics',
						    'videomae-small-finetuned-ssv2',
						    # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
						    'videomae-base-short',
						    'videomae-base-short-finetuned-kinetics',
						    'videomae-base',
						    'videomae-base-finetuned-kinetics',
						    'videomae-large',
						    'videomae-large-finetuned-kinetics',
						    'videomae-huge-finetuned-kinetics',
						    # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
						    'videomae-base-short-ssv2',
						    'videomae-base-short-finetuned-ssv2',
						    'videomae-base-ssv2',
						    'videomae-base-finetuned-ssv2',
						]

						# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
						if model_name == "videomae-small-finetuned-kinetics":
												lowerCAmelCase   :str						=			torch.Size([1, 4_00] )
												lowerCAmelCase   :int						=			torch.tensor([-0.9_291, -0.4_061, -0.9_307] )
						elif model_name == "videomae-small-finetuned-ssv2":
												lowerCAmelCase   :List[str]						=			torch.Size([1, 1_74] )
												lowerCAmelCase   :Tuple						=			torch.tensor([0.2_671, -0.4_689, -0.8_235] )
						elif model_name == "videomae-base":
												lowerCAmelCase   :Any						=			torch.Size([1, 14_08, 15_36] )
												lowerCAmelCase   :Optional[int]						=			torch.tensor([[0.7_739, 0.7_968, 0.7_089], [0.6_701, 0.7_487, 0.6_209], [0.4_287, 0.5_158, 0.4_773]] )
						elif model_name == "videomae-base-short":
												lowerCAmelCase   :Union[str, Any]						=			torch.Size([1, 14_08, 15_36] )
												lowerCAmelCase   :List[Any]						=			torch.tensor([[0.7_994, 0.9_612, 0.8_508], [0.7_401, 0.8_958, 0.8_302], [0.5_862, 0.7_468, 0.7_325]] )
												# we verified the loss both for normalized and unnormalized targets for this one
												lowerCAmelCase   :int						=			torch.tensor([0.5_142] ) if config.norm_pix_loss else torch.tensor([0.6_469] )
						elif model_name == "videomae-large":
												lowerCAmelCase   :List[Any]						=			torch.Size([1, 14_08, 15_36] )
												lowerCAmelCase   :Dict						=			torch.tensor([[0.7_149, 0.7_997, 0.6_966], [0.6_768, 0.7_869, 0.6_948], [0.5_139, 0.6_221, 0.5_605]] )
						elif model_name == "videomae-large-finetuned-kinetics":
												lowerCAmelCase   :int						=			torch.Size([1, 4_00] )
												lowerCAmelCase   :Tuple						=			torch.tensor([0.0_771, 0.0_011, -0.3_625] )
						elif model_name == "videomae-huge-finetuned-kinetics":
												lowerCAmelCase   :int						=			torch.Size([1, 4_00] )
												lowerCAmelCase   :Union[str, Any]						=			torch.tensor([0.2_433, 0.1_632, -0.4_894] )
						elif model_name == "videomae-base-short-finetuned-kinetics":
												lowerCAmelCase   :Dict						=			torch.Size([1, 4_00] )
												lowerCAmelCase   :Any						=			torch.tensor([0.6_588, 0.0_990, -0.2_493] )
						elif model_name == "videomae-base-finetuned-kinetics":
												lowerCAmelCase   :Any						=			torch.Size([1, 4_00] )
												lowerCAmelCase   :Optional[Any]						=			torch.tensor([0.3_669, -0.0_688, -0.2_421] )
						elif model_name == "videomae-base-short-ssv2":
												lowerCAmelCase   :str						=			torch.Size([1, 14_08, 15_36] )
												lowerCAmelCase   :Dict						=			torch.tensor([[0.4_712, 0.5_296, 0.5_786], [0.2_278, 0.2_729, 0.4_026], [0.0_352, 0.0_730, 0.2_506]] )
						elif model_name == "videomae-base-short-finetuned-ssv2":
												lowerCAmelCase   :Optional[Any]						=			torch.Size([1, 1_74] )
												lowerCAmelCase   :Any						=			torch.tensor([-0.0_537, -0.1_539, -0.3_266] )
						elif model_name == "videomae-base-ssv2":
												lowerCAmelCase   :List[str]						=			torch.Size([1, 14_08, 15_36] )
												lowerCAmelCase   :int						=			torch.tensor([[0.8_131, 0.8_727, 0.8_546], [0.7_366, 0.9_377, 0.8_870], [0.5_935, 0.8_874, 0.8_564]] )
						elif model_name == "videomae-base-finetuned-ssv2":
												lowerCAmelCase   :Any						=			torch.Size([1, 1_74] )
												lowerCAmelCase   :Union[str, Any]						=			torch.tensor([0.1_961, -0.8_337, -0.6_389] )
						else:
												raise ValueError(F"""Model name not supported. Should be one of {model_names}""" )

						# verify logits
						assert logits.shape == expected_shape
						if "finetuned" in model_name:
												assert torch.allclose(logits[0, :3]     ,      a__     ,      atol=1e-4 )
						else:
												print('Logits:'     ,      logits[0, :3, :3] )
												assert torch.allclose(logits[0, :3, :3]     ,      a__     ,      atol=1e-4 )
						print('Logits ok!' )

						# verify loss, if applicable
						if model_name == "videomae-base-short":
												lowerCAmelCase   :Optional[Any]						=			outputs.loss
												assert torch.allclose(a__     ,      a__     ,      atol=1e-4 )
												print('Loss ok!' )

						if pytorch_dump_folder_path is not None:
												print(F"""Saving model and image processor to {pytorch_dump_folder_path}""" )
												image_processor.save_pretrained(a__ )
												model.save_pretrained(a__ )

						if push_to_hub:
												print('Pushing to the hub...' )
												model.push_to_hub(a__     ,      organization='nielsr' )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--checkpoint_url',
		    default='https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4',
		    type=str,
		    help=(
		        'URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct'
		        ' download link.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path',
		    default='/Users/nielsrogge/Documents/VideoMAE/Test',
		    type=str,
		    help='Path to the output PyTorch model directory.',
		)
		parser.add_argument('--model_name', default='videomae-base', type=str, help='Name of the model.')
		parser.add_argument(
		    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
		)

		__SCREAMING_SNAKE_CASE   =   parser.parse_args()
		convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)







"""simple docstring"""


import itertools
import math


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5     ,      int(math.sqrt(a__ ) + 1 )     ,      6 ):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True


def        UpperCAmelCase      (					):





						'''simple docstring'''






						lowerCAmelCase   :str						=			2
						while True:
												if is_prime(a__ ):
																		yield num
												num += 1


def        UpperCAmelCase      (					a__ = 1_00_01 ):





						'''simple docstring'''






						return next(itertools.islice(prime_generator()     ,      nth - 1     ,      a__ ) )


if __name__ == "__main__":
		print(F"""{solution() = }""")